SOTAVerified

Intrusion Detection

Intrusion Detection is the process of dynamically monitoring events occurring in a computer system or network, analyzing them for signs of possible incidents and often interdicting the unauthorized access. This is typically accomplished by automatically collecting information from a variety of systems and network sources, and then analyzing the information for possible security problems.

Source: Machine Learning Techniques for Intrusion Detection

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 401–425 of 800 papers

TitleStatusHype
Jasmine: A New Active Learning Approach to Combat Cybercrime—0
Joint Semantic Transfer Network for IoT Intrusion Detection—0
Kernel density estimation based sampling for imbalanced class distribution—0
Keystroke Patterns as Prosody in Digital Writings: A Case Study with Deceptive Reviews and Essays—0
KiNETGAN: Enabling Distributed Network Intrusion Detection through Knowledge-Infused Synthetic Data Generation—0
KnowGraph: Knowledge-Enabled Anomaly Detection via Logical Reasoning on Graph Data—0
KnowML: Improving Generalization of ML-NIDS with Attack Knowledge Graphs—0
Large Language Models for Cyber Security: A Systematic Literature Review—0
Large Language Models in Wireless Application Design: In-Context Learning-enhanced Automatic Network Intrusion Detection—0
Late Breaking Results: Scalable and Efficient Hyperdimensional Computing for Network Intrusion Detection—0
Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT—0
LBDMIDS: LSTM Based Deep Learning Model for Intrusion Detection Systems for IoT Networks—0
Learning automata based SVM for intrusion detection—0
Learning-Based Detection of Malicious Volt-VAr Control Parameters in Smart Inverters—0
Learning detectors of malicious web requests for intrusion detection in network traffic—0
Learning in Multiple Spaces: Few-Shot Network Attack Detection with Metric-Fused Prototypical Networks—0
Learning Privately from Multiparty Data—0
Learning to Detect: A Data-driven Approach for Network Intrusion Detection—0
Learning With Differential Privacy—0
LEMDA: A Novel Feature Engineering Method for Intrusion Detection in IoT Systems—0
LENS-XAI: Redefining Lightweight and Explainable Network Security through Knowledge Distillation and Variational Autoencoders for Scalable Intrusion Detection in Cybersecurity—0
Leveraging Planning Landmarks for Hybrid Online Goal Recognition—0
Leveraging Siamese Networks for One-Shot Intrusion Detection Model—0
LGTBIDS: Layer-wise Graph Theory Based Intrusion Detection System in Beyond 5G—0
LSTM-Based System-Call Language Modeling and Robust Ensemble Method for Designing Host-Based Intrusion Detection Systems—0
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All datasetsCICIDS2017CIC-DDoSCIC-DoSUNSW-NB15

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAccuracy (%)98.13—Unverified
2K-Nearest NeighborsAccuracy (%)98.07—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-PCAAUC0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-IBAUC0.95—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-AEAUC0.9—Unverified